Datasets:

michelecafagna26
/

hl

	# coding=utf-8
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""High-Level dataset."""


	import json
	from pathlib import Path

	import datasets


	_CITATION = """\
	@inproceedings{Cafagna2023HLDG,
	title={HL Dataset: Grounding High-Level Linguistic Concepts in Vision},
	author={Michele Cafagna and Kees van Deemter and Albert Gatt},
	year={2023}
	}
	"""

	_DESCRIPTION = """\
	High-level Dataset
	"""

	# github link
	_HOMEPAGE = "https://github.com/michelecafagna26/HL-dataset"

	_LICENSE = "Apache 2.0"

	_IMG = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/images.tar.gz"
	_TRAIN = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/annotations/train.jsonl"
	_TEST = "https://huggingface.co/datasets/michelecafagna26/hl/resolve/main/data/annotations/test.jsonl"



	class HL(datasets.GeneratorBasedBuilder):
	"""High Level Dataset."""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	features = datasets.Features(
	{
	"file_name": datasets.Value("string"),
	"image": datasets.Image(),
	"scene": datasets.Sequence(datasets.Value("string")),
	"action": datasets.Sequence(datasets.Value("string")),
	"rationale": datasets.Sequence(datasets.Value("string")),
	"object": datasets.Sequence(datasets.Value("string")),
	"confidence": {
	"scene": datasets.Sequence(datasets.Value("float32")),
	"action": datasets.Sequence(datasets.Value("float32")),
	"rationale": datasets.Sequence(datasets.Value("float32")),
	},
	"purity": {
	"scene": datasets.Sequence(datasets.Value("float32")),
	"action": datasets.Sequence(datasets.Value("float32")),
	"rationale": datasets.Sequence(datasets.Value("float32")),
	},
	"diversity": {
	"scene": datasets.Value("float32"),
	"action": datasets.Value("float32"),
	"rationale": datasets.Value("float32"),
	},
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	image_files = dl_manager.download(_IMG)
	annotation_files = dl_manager.download_and_extract([_TRAIN, _TEST])
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"annotation_file_path": annotation_files[0],
	"images": dl_manager.iter_archive(image_files),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"annotation_file_path": annotation_files[1],
	"images": dl_manager.iter_archive(image_files),
	},
	),
	]

	def _generate_examples(self, annotation_file_path, images):

	idx = 0

	#assert Path(annotation_file_path).suffix == ".jsonl"

	with open(annotation_file_path, "r") as fp:
	metadata = {json.loads(item)['file_name']: json.loads(item) for item in fp}

	# This loop relies on the ordering of the files in the archive:
	# Annotation files come first, then the images.
	for img_file_path, img_obj in images:

	file_name = Path(img_file_path).name

	if file_name in metadata:
	yield idx, {
	"file_name": file_name,
	"image": {"path": img_file_path, "bytes": img_obj.read()},
	"scene": metadata[file_name]['captions']['scene'],
	"action": metadata[file_name]['captions']['action'],
	"rationale": metadata[file_name]['captions']['rationale'],
	"object": metadata[file_name]['captions']['object'],
	"confidence": metadata[file_name]['confidence'],
	"purity": metadata[file_name]['purity'],
	"diversity": metadata[file_name]['diversity'],
	}
	idx += 1